Abstract's details

Mapping Internal Tides from Satellite Altimetry without Blind Spots

Zhongxiang Zhao (University of Washington, United States)

Event: 2019 Ocean Surface Topography Science Team Meeting

Session: Tides, internal tides and high-frequency processes

Presentation type: Type Poster

Contribution: not provided


Satellite altimetry may be the only practical technique for observing internal tides on the global scale. However, it is a challenge to mapping the global complicated internal tide field using satellite altimeter data, mainly due to the low spatiotemporal sampling rate of nadir-looking satellite altimetry. The next-generation wide-swath altimetry will also have low temporal resolution. Therefore, it is important to develop new techniques for better mapping internal tides from the current- and next-generation satellite altimetry. This study addresses a problem caused by along-track high-pass filtering in previous studies, which is used to remove long-wavelength nontidal noise and barotropic tidal residual. However, the filter removes internal tides having large angles with respect to satellite ground tracks and thus causes blind spots in the resultant internal tide field. Satellite ground tracks are generally in south-north direction at low latitudes; therefore, westbound and eastbound internal tides are either underestimated or totally missed. This study presents a new technique that replaces the one-dimensional (1D) along-track high-pass filter with a two-dimensional (2D) bandpass filter. The latter extracts internal tides in all directions without blind spots. Westbound and eastbound internal tides can be retrieved by the new technique. The new internal tide models are presented and compared with previous models. The improvement is shown by SSH variance reduction explained by applying different models to independent Cryosat-2 altimeter data. However, the new technique makes no improvement in boundary current systems or energetic equatorial zones. The application of this new technique to other observational and modeled datasets are also demonstrated.


Poster show times:

Room Start Date End Date
The Gallery Tue, Oct 22 2019,16:15 Tue, Oct 22 2019,18:00
The Gallery Thu, Oct 24 2019,14:00 Thu, Oct 24 2019,15:45
Zhongxiang Zhao
University of Washington
United States